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A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

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Abstract

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

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Acknowledgements

This work is supported by the “Natural Science Foundation of China” (No. 61806047). We thank Miss Zixian Li and Mr. Guoxian Li for their important discussion.

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Authors and Affiliations

  1. Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China

    Jiawei Zhang, Chen Li, Md Mamunur Rahaman, Yudong Yao, Pingli Ma, Jinghua Zhang, Xin Zhao, Tao Jiang & Marcin Grzegorzek

  2. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Yudong Yao

  3. School of Resources and Civil Engineering, Northeastern University, Shenyang, 110004, China

    Xin Zhao

  4. School of Control Engineering, Chengdu University of Information Technology, Chengdu, 610225, China

    Tao Jiang

  5. Institute of Medical Informatics, University of Luebeck, Luebeck, 23538, Germany

    Jinghua Zhang & Marcin Grzegorzek

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  1. Jiawei Zhang

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  3. Md Mamunur Rahaman

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Zhang, J., Li, C., Rahaman, M.M.et al. A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches.Artif Intell Rev55, 2875–2944 (2022). https://doi.org/10.1007/s10462-021-10082-4

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